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Background: Survival from out-of-hospital cardiac arrest (OHCA) is generally poor and varies by geography. Variability in automated external defibrillator (AED) locations may be a contributing factor. To inform optimal placement of AEDs, we investigated AED access in a major US city relative to demographic and employment characteristics., Methods and Results: This was a retrospective analysis of a Philadelphia AED registry (2,559 total AEDs). The 2010 US Census and the Local Employment Dynamics database by ZIP code was used. Automated external defibrillator access was calculated as the weighted areal percentage of each ZIP code covered by a 400-m radius around each AED. Of 47 ZIP codes, only 9% (4) were high-AED-service areas. In 26% (12) of ZIP codes, less than 35% of the area was covered by AED service areas. Higher-AED-access ZIP codes were more likely to have a moderately populated residential area (P = .032), higher median household income (P = .006), and higher paying jobs (P =. 008)., Conclusions: The locations of AEDs vary across specific ZIP codes; select residential and employment characteristics explain some variation. Further work on evaluating OHCA locations, AED use and availability, and OHCA outcomes could inform AED placement policies. Optimizing the placement of AEDs through this work may help to increase survival., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Study Objective: Two decades ago, Philadelphia began allowing police transport of patients with penetrating trauma. We conduct a large, multiyear, citywide analysis of this policy. We examine the association between mode of out-of-hospital transport (police department versus emergency medical services [EMS]) and mortality among patients with penetrating trauma in Philadelphia., Methods: This is a retrospective cohort study of trauma registry data. Patients who sustained any proximal penetrating trauma and presented to any Level I or II trauma center in Philadelphia between January 1, 2003, and December 31, 2007, were included. Analyses were conducted with logistic regression models and were adjusted for injury severity with the Trauma and Injury Severity Score and for case mix with a modified Charlson index., Results: Four thousand one hundred twenty-two subjects were identified. Overall mortality was 27.4%. In unadjusted analyses, patients transported by police were more likely to die than patients transported by ambulance (29.8% versus 26.5%; OR 1.18; 95% confidence interval [CI] 1.00 to 1.39). In adjusted models, no significant difference was observed in overall mortality between the police department and EMS groups (odds ratio [OR] 0.78; 95% CI 0.61 to 1.01). In subgroup analysis, patients with severe injury (Injury Severity Score >15) (OR 0.73; 95% CI 0.59 to 0.90), patients with gunshot wounds (OR 0.70; 95% CI 0.53 to 0.94), and patients with stab wounds (OR 0.19; 95% CI 0.08 to 0.45) were more likely to survive if transported by police., Conclusion: We found no significant overall difference in adjusted mortality between patients transported by the police department compared with EMS but found increased adjusted survival among 3 key subgroups of patients transported by police. This practice may augment traditional care., (Copyright © 2014 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.)

Study Objective: Collective knowledge and coordination of vital interventions for time-sensitive conditions (ST-segment elevation myocardial infarction [STEMI], stroke, cardiac arrest, and septic shock) could contribute to a comprehensive statewide emergency care system, but little is known about population access to the resources required. We seek to describe existing clinical management strategies for time-sensitive conditions in Pennsylvania hospitals., Methods: All Pennsylvania emergency departments (EDs) open in 2009 were surveyed about resource availability and practice patterns for time-sensitive conditions. The frequency with which EDs provided essential clinical bundles for each condition was assessed. Penalized maximum likelihood regressions were used to evaluate associations between ED characteristics and the presence of the 4 clinical bundles of care. We used geographic information science to calculate 60-minute ambulance access to the nearest facility with these clinical bundles., Results: The percentage of EDs providing each of the 4 clinical bundles in 2009 ranged from 20% to 57% (stroke 20%, STEMI 32%, cardiac arrest 34%, sepsis 57%). For STEMI and stroke, presence of a board-certified/board-eligible emergency physician was significantly associated with presence of a clinical bundle. Only 8% of hospitals provided all 4 care bundles. However, 53% of the population was able to reach this minority of hospitals within 60 minutes., Conclusion: Reliably matching patient needs to ED resources in time-dependent illness is a critical component of a coordinated emergency care system. Population access to critical interventions for the time-dependent diseases discussed here is limited. A population-based planning approach and improved coordination of care could improve access to interventions for patients with time-sensitive conditions., (Copyright © 2013 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.)

Study Objective: We examine the validity of the emergency department (ED) occupancy rate as a measure of crowding by comparing it to the Emergency Department Work Index Score (EDWIN), a previously validated scale., Methods: A multicenter validation study was conducted according to ED visit data from 6 academic EDs for a 3-month period in 2005. Hourly ED occupancy rate (ie, total number of patients in ED divided by total number of licensed beds) and EDWIN scores were calculated. The correlation between the scales was determined and their validity evaluated by their ability to discriminate between hours when 1 or more patients left without being seen and hours when the ED was on ambulance diversion, using area under the curve (AUC) statistics estimated from the bootstrap method., Results: We calculated the ED occupancy rate and EDWIN for 2,208 consecutive hours at each of the 6 EDs. The overall correlation between the 2 scales was 0.58 (95% confidence interval [CI] 0.56 to 0.60). The ED occupancy rate (AUC=0.73; 95% CI 0.65 to 0.80) and the EDWIN (AUC=0.65; 95% CI 0.58 to 0.72) did not differ significantly in correctly identifying hours when patients left without being seen. The ED occupancy rate (AUC=0.78; 95% CI 0.75 to 0.80) and the EDWIN (AUC=0.70; 95% CI 0.59 to 0.81) performed similarly for ED diversion hours., Conclusion: The ED occupancy rate and the EDWIN classified leaving without being seen and ambulance diversion hours with moderate accuracy. Although the ED occupancy rate is not ideal, its simplicity makes real-time assessment of crowding feasible for more EDs nationwide.